This paper proposes an evaluation metric which derive the effectiveness of background modeling algorithms. Background modeling is a key process on developing visual surveillance systems. The requirement of adapting to dynamic environments has motivated researchers to modify existing background modeling algorithms and develop new algorithms with better adaptability. Having the algorithms developed, credentials of each of the algorithms have to be assessed to exploit their effectiveness. Various evaluation metrics have been used for evaluating the rate of foreground extraction, foreground detection, and overall accuracy. However, the rate of background detection has not been exploited by these metrics. Therefore, this paper would provide an insight to the existing evaluation metrics and introduce our proposed metric for estimating the rate of background detection. © 2016 IEEE.